land

Article Land Cover Influences on LST in Two Proposed Smart of India: Comparative Analysis Using Spectral Indices

Manish Ramaiah 1, Ram Avtar 1,2,* and Md. Mustafizur Rahman 1

1 Graduate School of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, Japan; [email protected] (M.R.); [email protected] (M.M.R.) 2 Faculty of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, Japan * Correspondence: [email protected]; Tel.: +81-11-706-2261

 Received: 30 June 2020; Accepted: 17 August 2020; Published: 24 August 2020 

Abstract: Elucidating the impact of Land Surface Temperature (LST) is an important aspect of . The impact of on LST has been widely studied to monitor the (UHI) phenomenon. However, the sensitivity of various urban factors such as urban green spaces (UGS), built-up area, and water bodies to LST is not sufficiently resolved for many urban settlements. By using remote sensing techniques, this study aimed to quantify the influence of urban factors on LST in the two traditional cities (i) and (ii) Tumkur of India, proposed to be developed as smart cities. Landsat were used to extract thematic and statistical information about urban factors using the Enhanced Built-up and Bareness Index (EBBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SAVI). The multivariate regression model revealed that the value of adjusted R2 was 0.716 with a standard error of 1.97 for Tumkur , while it was 0.698 with a standard error of 1.407 for Panaji city. The non-parametric correlation test brought out a strong negative correlation between MNDWI and LST with a value of 0.83 for Panaji, and between SAVI and LST with a value of 0.77 for Tumkur. The maximum percentage share of cooling surfaces are water bodies in Panaji with 35% coverage and green spaces in Tumkur with 25% coverage. Apparently, the UGS and water bodies can help in bringing down the LST, as well as facilitating healthy living conditions and aesthetic appeal. Therefore, the significance of ecosystem services (green spaces and water bodies) should be given priority in the decision-making process of sustainable and vibrant city development.

Keywords: SAVI; MNDWI; EBBI; LST; UGS; spectral indices; Tumkur; Panaji

1. Introduction Rapidly expanding urbanization taking place in an unplanned manner necessitates knowledge of the extent and shape of the settlement and ecological features [1,2] from the perspective. The concept of environmentally and ecologically vibrant city development emerged in early 1970 [3]. However, the relationship between urban landscape patterns and microclimate needs to be sufficiently understood [4] for developing urban areas more efficiently. In this regard, information on diverse patterns of intensity or spatial growth is essential to delineate both beneficial and adverse impacts on the urban environment [1,5]. Unplanned spatial growth of cities brings numerous environmental problems, among which the “urban heat island” (UHI) effect is a well-documented climatological effect of human activities on the urban environment [4,6,7]. The urban heat islands (UHI) result from the increased heat storage capacity of urban surfaces [8]. Typically, the concentric urban expansion patterns lead to intensive UHI [9–11]. The UHIs result to a large extent from the formation

Land 2020, 9, 292; doi:10.3390/land9090292 www.mdpi.com/journal/land Land 2020, 9, 292 2 of 21 of urban microclimates due to built-up areas, concrete zones, and high concentrations of various human activities [4,12,13]. Within the UHIs, the built-up areas are hotter than adjacent/rural areas [14], causing a local difference in temperatures, which hampers air quality and impacts the environment, thermal comfort, and peoples’ health. It also leads to increased energy consumption [15] for cooling homes and offices. UHIs trap atmospheric pollutants, contribute to increased urban smog formation, and generate socio-economic impacts affecting the quality of urban life [16,17]. Since urbanization transforms the land use/land cover (LULC) pattern by increasing the built-up areas, the energy balance gets modified, resulting in urban areas becoming warmer than the surrounding rural/less-urban areas [18,19]. Factors that contribute to increased UHI phenomenon include high building density, a reduction in urban green spaces (UGS), and increases in built-up spaces [20]. The ecological features, such as vegetation and water bodies, are highly sensitive to LST [21–24]. Hua et al., [4] demonstrated that LST increases with a decrease in vegetation and an increase in non-evaporative surfaces. The United States Environmental Protection Agency (US EPA, 2017) [25] reported that some serious health hazards such as general discomfort, respiratory difficulties, heat cramps, non-fatal heat stroke, and heat-related mortality are rising with the increase of thermal surfaces and a corresponding decrease in cooling surfaces. The Agency suggested that it is possible to reduce UHIs by increasing trees and vegetative cover and by installing green roofs. The increase of vegetation cover and water bodies or decrease in impervious surfaces can help to strengthen Green space Cool Island (GCI) effects [26,27]. The research community is able to characterize and examine the UHI-landscape relationship due to advances in thermal remote sensing, geographical information systems (GIS), and statistical methods. -makers and researchers have received valuable feedback from several studies carried out dealing with UHI analysis [8]. Besides air temperature, land surface temperature (LST) derived from remote sensing data is essential and highly reliable in identifying surface UHIs [28]. Nichol and To (2012) [29] reported that between the temperature data collected from urban weather stations (usually within/near a park/shading trees) and the LST derived from remote sensing, the latter can reliably spot the hottest and coolest areas. For inferring the impacts of rapid and/or unplanned urbanization, information on land use intensity in terms of built-up area or bare land in relation to the vegetation cover and water spread area is very useful and helps to recognize adverse impacts or useful outcomes due to planned urban land use patterns. Analyses of remote sensing data and GIS-based derivation of relevant indices have proven useful in recognizing the changes in land use/land cover (LULC) and deriving variations in LST. It is vital to investigate the crucial land dynamic processes which significantly contribute to the increase in LST and aggravation of the UHI effect [26]. In the absence of ground-based meteorological stations, the spatiotemporal assessment of LST using thermal remote sensing data can help to assess the LST changes and support policy-makers carrying out eco-sensitive city development [24,30]. As is widely known, remote sensing techniques can also aid in investigating the complex relationship between spatial parameters and thermal conditions over large areas along with updated spatial information in cost-effective ways [6,31,32]. The use of remote sensing indices for earth observation has been well acknowledged by remote sensing professionals. They are widely used for various applications such as detecting environmental changes [33], monitoring urban expansion [13,34], monitoring vegetation, and assessing water bodies and their impacts on LST [35–39]. However, there are no comparative assessments of different geographical locations and relationships of UGS, built-up area, and water bodies with LST. The predicted rate and intensity of climate change [40,41] and higher temperatures in UHIs leading to reduced thermal comfort and increase in energy consumption calls for urgently planning, developing, and maintaining the UGS as a major strategy “to adapt to and mitigate the expected continual increase in temperature” [42]. Thus the role of urban greenspace in moderating urban climates is vital [43–45]. Back in the 1990s itself, Semrau (1992) [46] and Rosenfeld et al., (1995) [47] emphasized that an effective way to reduce or alleviate the effects of UHIs is to increase tree cover area and density. With the above background, the purpose of this study was to assess the quantitative relationship of urban factors (built-up areas, vegetation cover, and water bodies) with LST using multivariate statistical Land 2020, 9, 292 3 of 21

Land 2020, 9, x FOR PEER REVIEW 3 of 21 analysis for two geographically disparate Indian cities: Tumkur and Panaji. As such, the smart city developmentstatistical analysis objectives for two in both geographically cities do not disparate showcase Indian how cities: land coverTumkur changes and Panaji. could As aff ectsuch, the the LST, UHIssmart formation, city development or living objectives conditions. in both As cities per do the not information showcase how posted land oncover the changes Tumkur could Smart affect City the LST, UHIs formation, or living conditions. As per the information posted on the Tumkur Smart website [48], in Tumkur city, approximately only 37% of the ‘smart city’ work is completed. Similarly, City website [48], in Tumkur city, approximately only 37% of the ‘smart city’ work is completed. only ~40% of such work packages are completed in Panaji (personal information). Similarly, only ~40% of such work packages are completed in Panaji (personal information). The developmental plans in both cities do not mention either LST or UHI as features of importance The developmental plans in both cities do not mention either LST or UHI as features of in the expansion and upkeep of smart cities. In this regard, this research is hoped to provide input importance in the expansion and upkeep of smart cities. In this regard, this research is hoped to forprovide inclusion input and for improving inclusion urban and planning.improving Thus,urban in planning. both cities, Thus, much in ofboth the cities, developmental much of the work needsdevelopmental to be done work not onlyneeds to to consider be done not the only impact to consider of increasing the impact LST andof increasing UHI effects LST butandalso UHIon maintenanceeffects but also and on creation maintenance of additional and creation UGS. of In addi viewtional of this, UGS. this In studyview of aimed this, this to examinestudy aimed how to the landexamine cover featureshow the land affect cover the LST, features which affect in turnthe LST, is to which be factored in turn in is for to improvingbe factored andin for sustaining improving the livingand comfort.sustaining The the findingsliving comfort. of this The study findings are useful of this to study recognize are useful the sensitivityto recognize of the urban sensitivity ecological of featuresurban (greenecological spaces features and (green water spaces bodies) and and water their bodies) influence and ontheir LST. influence By assessing on LST. the By ecologicallyassessing andthe environmentally ecologically and environmentally vibrant cities, which vibrant are cities, also wh proposedich are also to proposed be future to smart be future cities, smart this cities, study hopesthis tostudy contribute hopes to to contribute the formulation to the formulatio of policy frameworksn of policy frameworks for sustainable for sustainable and livable and community livable developmentcommunity development (SDGs 11) [49 (SDGs]. It would 11) [49]. also It would provide also information provide information for prioritizing for prioritizing the action the action plans to mitigateplans to the mitigate adverse the impact adverse of impact the destruction of the destruct of ecologicalion of ecological features features due to thedue rapid to the growth rapid growth of many citiesof many in developing cities in developing countries. countries.

2. Study2. Study Area Area PanajiPanaji and and Tumkur Tumkur citiescities were were chosen chosen for for this this study study by considering by considering several several factors, factors, such as such their as theirgeographic geographic location, location, population, population, and and green green space space cover. cover. Apart Apart from from the the disparate disparate geographical geographical locationslocations of Panajiof Panaji (a coastal (a coastal city) city) and Tumkurand Tumkur (an interior (an interior city), various city), various other factors other suchfactors as populationsuch as size,population gross domestic size, gross product domestic (GDP), pr andoduct climatic (GDP), factors and climatic were considered. factors were In considered. 2015, both these In 2015, cities both were designatedthese cities to were be developed designated as to smart be developed cities under as sm theart Nationalcities under Smart the CitiesNational Mission Smart [Cities50]. Panaji, Mission as a [50]. Panaji, as a coastal city, is highly vulnerable to sea-level rise issues via climate change impacts. coastal city, is highly vulnerable to sea-level rise issues via climate change impacts. Located in the Located in the interior part of India, Tumkur city is rapidly industrializing and recognized as an interior part of India, Tumkur city is rapidly industrializing and recognized as an important National important National Investment and Manufacturing Zone (NIMZ) by the Government of India. Panaji Investment and Manufacturing Zone (NIMZ) by the Government of India. Panaji city is the capital of city is the capital of Goa state and located at a latitude of 15°29′48.3972″ N and a longitude of Goa state and located at a latitude of 15 29 48.3972” N and a longitude of 73 49 40.1772” E. Tumkur city 73°49′40.1772″ E. Tumkur city is located◦ 0 in the southeastern part of Karnataka◦ 0 state at a latitude of is located13°20′17.7468 in the″ southeasternN and a longitude part ofof Karnataka77°6′5.0760 state″ E. Figure at a latitude 1 illustrates of 13 the◦20 0locations17.7468” of N the and Panaji a longitude and of 77Tumkur◦605.0760” cities E. in FigureIndia. 1 illustrates the locations of the Panaji and Tumkur cities in India.

FigureFigure 1. 1.Map Map showing showing thethe citycity areas of Panaji and and Tumkur Tumkur considered considered for for this this study. study. Land 2020, 9, 292 4 of 21

In Panaji city [51], despite its strength of compact form and mixed land-use, the most noted weakness listed is a lack of adequate and reliable public facilities, both within Panaji and for connecting outgrowth areas to Panaji. The main opportunity is the use of mangroves and a network of creeks as natural resources. These blue-green can provide ecosystem services to help mitigate the impacts of threats of urban development and climate change events like sea level rise. Due to rapid urbanization, the city’s environmental resources are noted to get adversely impacted, potentially lowering the city’s resilience to various natural disasters. Notably, there are serious concerns of heavy losses as well as unsustainable and chaotic situations due to many parameters (impending sea level rise, severe traffic congestion, troubled pedestrian mobility, increased population and vehicles, citizens’ health and livability, and resulting air/noise ). It is cautioned that the eventual collapse of the existing network will occur if appropriate measures are not taken. However, there is no mention in the city development plan on what the LULC changes can bring about or how the new developments would create UHIs or affect the LST. The coastal city of Panaji experiences a tropical climate, with the annual average temperature being 27.4 ◦C[52]. Temperatures start rising from January to a peak of around 34 ◦C in April, the hottest month (Figure2a). Thereafter, it declines during the monsoon months of June–September. The average annual rainfall of Panaji is 2774 mm. The city receives over 85% of the total rainfall during June–September. January is the coldest month with a temperature below 26 ◦C. The predominant climate in Tumkur is defined as a local steppe climate, with very little rainfall throughout the year [53] and an annual mean temperature of 24.4 ◦C (Figure2b). The average annual rainfall is 630 mm in Tumkur and October is the wettest month with 140 mm of precipitation [53]. A comparative account of the major features of these cities is furnished in Table1.

Table1. Details of different demographic, meteorological, and other parameters from Panaji and Tumkur cities proposed to be developed as smart cities [50].

Parameters Panaji Tumkur City area (km2) 21.60 48.60 Geographic location Coastal Interior Land scape Heterogenous Majorly plain Population (2018 estimate) 255,381 512,000 City GDP (Billion US$) 9.22 (2016) 1.14 (2014)

With 17 parks in the maintained by the Corporation of the City of Panaji (CCP) and the Forest Department, Panaji city (excluding the areas under the CCP) has about 0.80 km2 under green cover [54]. The natural marshy lands and mangroves along the waterfront add to natural, non-curated green areas and act as barriers to prevent monsoonal flooding. The Bhagwan Mahaveer Park maintained by the Forest Department, is the largest green patch in the city. The other 16, mostly small-sized parks plus public gardens, including the forest nursery and roadside plants, add up to about 5% of the green cover. Land 2020, 9, x FOR PEER REVIEW 4 of 21

In Panaji city [51], despite its strength of compact form and mixed land-use, the most noted weakness listed is a lack of adequate and reliable public transport facilities, both within Panaji and for connecting outgrowth areas to Panaji. The main opportunity is the use of mangroves and a network of creeks as natural resources. These blue-green infrastructures can provide ecosystem services to help mitigate the impacts of threats of urban development and climate change events like sea level rise. Due to rapid urbanization, the city’s environmental resources are noted to get adversely impacted, potentially lowering the city’s resilience to various natural disasters. Notably, there are serious concerns of heavy losses as well as unsustainable and chaotic situations due to many parameters (impending sea level rise, severe congestion, troubled pedestrian mobility, increased population and vehicles, citizens’ health and livability, and resulting air/noise pollution). It is cautioned that the eventual collapse of the existing infrastructure network will occur if appropriate measures are not taken. However, there is no mention in the city development plan on what the LULC changes can bring about or how the new developments would create UHIs or affect the LST. The coastal city of Panaji experiences a tropical climate, with the annual average temperature being 27.4 °C [52]. Temperatures start rising from January to a peak of around 34 °C in April, the hottest month (Figure 2a). Thereafter, it declines during the monsoon months of June–September. The average annual rainfall of Panaji is 2774 mm. The city receives over 85% of the total rainfall during June–September. January is the coldest month with a temperature below 26 °C. The predominant climate in Tumkur is defined as a local steppe climate, with very little rainfall throughout the year [53] and an annual mean temperature of 24.4 °C (Figure 2b). The average annual rainfall is 630 mm in Tumkur and October is the wettest month with 140 mm of precipitation [53]. A comparative account of the major features of these cities is furnished in Table 1.

Table 1. Details of different demographic, meteorological, and other parameters from Panaji and Tumkur cities proposed to be developed as smart cities [50].

Parameters Panaji Tumkur City area (km2) 21.60 48.60 Geographic location Coastal Interior Land scape Heterogenous Majorly plain Land 2020, 9, 292 Population (2018 estimate) 255,381 512,000 5 of 21 City GDP (Billion US$) 9.22 (2016) 1.14 (2014)

(a) Panaji Climate

C) 31 1000 ◦ 30 800 29 28 600 27 26 400 25 200 24 23 0 Monthly average rainfall (mm) JFMAMJJASOND Monthly average temperature ( Months Land 2020, 9, x FOR PEER REVIEW Temperature (◦C) Rainfall (mm) 5 of 21

(b) Tumkur Climate

C) 30 160 ◦ 140 25 120 20 100 15 80 60 10 40 5 20

0 0 Monthly average rainfall (mm) JFMAMJJASOND Monthly average temperature ( Months Temperature (◦C) Rainfall (mm)

FigureFigure 2. 2. MonthlyMonthly averages averages of of temperature temperature and and rainfall rainfall data of ( aa)) Panaji Panaji and and ( (b)) Tumkur. Tumkur.

WithIn Tumkur 17 parks city, in under the city the limits Smart maintained City Initiative, by the there Corporation are plans toof increase the City the of Panaji number (CCP) (/area) and of thegreen Forest spaces. Department, There are fourPanaji large-sized city (excludi parksng (the5 haareas ( 0.05 under km 2the)) in CCP) the city has maintained about 0.80 bykm Tumkur2 under ≥ ≥ greenUrban cover Development [54]. The Authoritynatural marshy (TUDA). lands A 10and ha mangroves (ha) (0.1 km along2) newly the waterfront developed add Amanikere to natural, Park non- on curatedthe area green recovered areas by and landfilling act as barriers the Amanikere to prevent irrigation monsoonal tank. flooding. Further, The new Bhagwan roads being Mahaveer laid under Park maintainedthe Smart City by Developmentthe Forest Department, Initiative have is the walk larg pathsest green and divider patch lanes.in the Suitablecity. The plantations other 16, onmostly both small-sizedsides of, and parks on, the plus divider public in gardens, the middle including are being th made.e forest Unlike nursery the and coastal roadside city of plants, Panaji add where up the to aboutmonsoonal 5% of rains the green help thecover. green spaces to sustain for quite long periods with once a fortnight watering, TumkurIn Tumkur city in thecity, interior under the region Smart receives City Initiative, insufficient there rainfall. are plans As to a result,increase the the existing number UGS (/area) need of wateringgreen spaces. at least There once are every four weeklarge-sized in suffi parkscient quantities(≥5 ha (≥0.05 to meet km2)) up in losses, the city including maintained those by occurring Tumkur Urbanthrough Development dispersals into Authority the soil. (TUDA). A 10 ha (ha) (0.1 km2) newly developed Amanikere Park on the areaPanaji recovered and Tumkur by landfilling are part the of theAmanikere National irri Smartgation Cities tank. Mission Further, by ne thew roads Government being laid of under India theto makeSmart them City citizen-friendlyDevelopment Initiative and sustainable have walk [50 paths]. The and Smart divider Cities lanes. Mission Suitable initiated plantations in 2015 on bothhas inclusivesides of, and aims on, by the considering divider in the all middle factors are essential being made. for sustainableUnlike the coastal urban city development of Panaji where [55]. theNecessary monsoonal infrastructure rains help elements the green in aspaces smart cityto sust listedain by for Prakash quite long [55] includeperiods adequatewith once water a fortnight supply, watering,stable assured Tumkur electricity city in the supply, interior sanitation region receives (including insufficient solid waste rainfall. and As wastewater a result, the management), existing UGS needefficient watering public transport,at least once ease every of urban week mobility, in sufficient affordable quantities housing, to ITmeet connectivity up losses, and including digitalization, those occurringgood , through dispersals sustainable into environment, the soil. safety and security of citizens, good healthcare, and educationPanaji and/employment Tumkur are opportunities.part of the National Smart Cities Mission by the Government of India to make them citizen-friendly and sustainable [50]. The Smart Cities Mission initiated in 2015 has inclusive aims by considering all factors essential for sustainable urban development [55]. Necessary infrastructure elements in a smart city listed by Prakash [55] include adequate water supply, stable assured electricity supply, sanitation (including solid waste and wastewater management), efficient public transport, ease of urban mobility, affordable housing, IT connectivity and digitalization, good governance, sustainable environment, safety and security of citizens, good healthcare, and education/employment opportunities.

3. Data and Methods With the introduction of thermal remote sensing, LST information is available from a series of satellite sensors (such as Landsat, MODIS, and ASTER) that cover a wide range of the Earth’s surface. Compared to air temperatures collected from weather stations, thermal imagery provides full spatial coverage at various temporal scales [56]. Figure 3 shows the flowchart of the methodology adopted in this study. Land 2020, 9, 292 6 of 21

3. Data and Methods With the introduction of thermal remote sensing, LST information is available from a series of satellite sensors (such as Landsat, MODIS, and ASTER) that cover a wide range of the Earth’s surface. Compared to air temperatures collected from weather stations, thermal imagery provides full spatial coverage at various temporal scales [56]. Figure3 shows the flowchart of the methodology adopted in this study. Land 2020, 9, x FOR PEER REVIEW 6 of 21

FigureFigure 3. 3. FlowFlow chartchart of the methodology used used in in this this study. study.

3.1.3.1. Satellite Satellite Data Data Landsat-8Landsat-8 satellite satellite data data was was used used to evaluateto evaluate the responsethe response of di ffoferent different land coversland covers on LST. on Landsat LST. 8 satelliteLandsat 8 has satellite Operational has Operational Land Imager Land Imager (OLI) and (OLI) Thermal and Thermal Infrared Infrared Sensor Sensor (TIR). (TIR). There There are are nine spectralnine spectral bands bands from from bands bands 1 to 1 9 to and 9 and two two spectral spectral bands bands from from bands bands 1010 to 11 in in OLI OLI and and TIRS TIRS sensors,sensors, respectively. respectively. BandsBands 1010 andand 1111 provideprovide atmospheric rectifications rectifications for for the the thermal thermal inferred inferred datadata [57 [57].]. Two Two Landsat-8 Landsat-8 OLI OLI/TIRS/TIRS datadata ofof the Tumkur and Panaji Panaji areas areas in in 2019 2019 were were acquired acquired from from https:https://earthexplorer.usgs.gov/[58].//earthexplorer.usgs.gov/ [58]. TableTable2 2 provides provides detailsdetails ofof Landsat-8Landsat-8 da datata used used in in this this study. study.

TableTable 2. 2.Path Path and and acquisitionacquisition detailsdetails of Land Landsat-8sat-8 data data from from the the study study areas. areas. Path/Tiles Time Acquisition Date Cloud Coverage Location Path/Tiles Time Acquisition Date Cloud Coverage Location 144/51 11:10:20.54 AM 14 April 2019 3% Tumkur 144/51147/49 11:10:20.54 11:28:12.31 AM AM 1418 AprilMarch 2019 2019 1% 3% Panaji Tumkur 147/49 11:28:12.31 AM 18 March 2019 1% Panaji 3.2. Field Data 3.2. FieldIn Datathis study, field surveys were conducted during January–February 2019 to collect ground- truth data for the LULC classification and validation. In the field survey locations of the UGS, built- In this study, field surveys were conducted during January–February 2019 to collect ground-truth up areas were marked with the help of a Global Positioning System (GPS) device (Gamin 60x). data for the LULC classification and validation. In the field survey locations of the UGS, built-up areas Various city development offices were also visited to collect secondary information about the were marked with the help of a Global Positioning System (GPS) device (Gamin 60x). Various city landscape, UGS, and city plans. Field surveys were aimed to understand the problems related to development offices were also visited to collect secondary information about the landscape, UGS, and city LULC patterns in these urban areas and management plans in place to address these problems in plans.both Fieldof these surveys upcoming were aimedsmart tocities. understand the problems related to LULC patterns in these urban areas and management plans in place to address these problems in both of these upcoming smart cities. 3.3. Methodology

3.3.1. Image Pre-Processing

The analysis in this study only included the ~98 km2 buffer area from both city centers and Landsat data were clipped for further analysis. Atmospheric correction was done prior to image processing. The aim of atmospheric correction was to derive a good estimate of the true at-ground upwelling radiance [59]. The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubus) model was implemented in the ENVI 5.3 platform provided by Harris Corporation in Melbourne, FL, USA.

Land 2020, 9, 292 7 of 21

3.3. Methodology

3.3.1. Image Pre-Processing The analysis in this study only included the ~98 km2 buffer area from both city centers and Landsat data were clipped for further analysis. Atmospheric correction was done prior to image processing. The aim of atmospheric correction was to derive a good estimate of the true at-ground upwelling radiance [59]. The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubus) model was implemented in the ENVI 5.3 platform provided by Harris Corporation in Melbourne, FL, USA.

3.3.2. Enhanced Built-Up and Bareness Index (EBBI) Remote sensing techniques provide an efficient and cost-effective approach to monitor the expansion of the built-up area, in comparison to other traditional approaches [13]. Previous studies used different indices for the extraction of desired land features. For example, the Normalized Difference Built-up Index (NDBI) was proposed by Zha [60] for Landsat Thematic Mapper (TM) images, the Built-up Area Extraction Index (BAEI) was proposed by Bouzekri [61] for highlighting built-up areas in Landsat-8 image, improved NDBI was proposed by He [62] as a semi-automatic approach to extract built-up area, and the Enhanced Built-Up and Bareness Index (EBBI) for highlighting built-up land and bare land was proposed by As-syakur et al. [34]. Application of the aforementioned indices depends on the study purpose, surface characteristics, accuracy level, and satellite image physiognomies. In this study, we used the EBBI index for extracting built-up and bare land areas. A recent study by Li et al., [63] revealed that EBBI is an effective method for extracting built-up area and bare land cover. In view of this, to distinguish the effect of LST on vegetation cover from the land cover altered due to human-induced activities (built-up) and bare land together, we used the EBBI index for extracting built-up and bare land. Hence, both bare land and built-up area were found to be resulting from urban activities. EBBI is derived using the following equation:

(SWIR1 NIR) EBBI = − (1) (10 root(SWIR1 + TIR)) ∗ where SWIR1, NIR, and TIR represent the band-6, band-5, and band-10 of Landsat-8 data, respectively.

3.3.3. Modified Normalized Difference Water Index (MNDWI) Waterbodies can be extracted by using the spectral information of satellite images. Compared to other surface objects, water bodies show a weak spectral reflectance in most of the wavelengths. In this research, we used the band threshold method using band-3 (Green) and band-6 (Mid–Infrared/SWIR1) of Landsat-8 images proposed by Xu (2006) which is the modified index of NDWI proposed by McFeeters (1996) for highlighting water bodies [33], who suggested that water information using the NDWI is often mixed with built-up land noise, resulting in an overestimation of extracted water bodies’ areas. MNDWI can provide better results as compared to NDWI. Therefore, MNDWI was used in this study. MNDWI is derived from the following equation:

(Green SWIR1) MNDWI = − . (2) (Green + SWIR1)

3.3.4. Soil Adjusted Vegetation Index (SAVI) The Soil Adjusted Vegetation Index (SAVI) is a useful index for extracting vegetation information. It was proposed by Huete [64], takes into account the optical soil properties on the plant canopy reflectance, and gathers information for a small amount of vegetation. The use of SAVI is an approach by which spectral indices are calibrated, so that soil substrate variations are effectively normalized without influencing vegetation measures [64]. An understanding of soft surface spectral properties, as well Land 2020, 9, 292 8 of 21 as their behavior and interactions with plant life and water, is crucial to development [65]. Therefore an ‘adjustment factor’ is used for measuring SAVI with varying vegetation density. A single adjustment factor (L = 0.5) was adopted in this analysis to shrink soft (moisture, organic inputs, erosion, and cultivation) noise considerably throughout the range in vegetation densities. SAVI, involving a constant L to the NDVI equation with a range of 1 to +1, is expressed as follows: − ! NIR + Red SAVI = (1 + L). (3) (NIR + Red + L) ∗

Two or three optimal adjustments for L constant (L = 1 for low vegetation densities; L = 0.5 for intermediate vegetation densities; L = 0.35 for higher densities) were suggested by Huete [64] wherein NIR and Red represent the band-5 and band-4 of Landsat-8 data, respectively.

3.3.5. Land Surface Temperature (LST) The land surface temperature was calculated using the standard methodology. Based on Weng et al., [12], a two-step process was followed to derive the brightness temperature of the land. The DN values of each Landsat image band were scaled from the total radiance calculated to byte values before media output using the gain and bias (offset) values given for each group. The DN values can be transformed back to the radiance units using the following formula:

Radiance (Lγ) = M Band 10 + A (4) L ∗ L where ML represents the band-specific multiplicative rescaling factor and AL represents the band-specific additive rescaling factor. ML and AL can be obtained from the header file of the satellite data. Once the DN was converted to radiance values, we calculated the brightness temperature (Bt) using the following equation:      k2  Bt =    + 1 273.15 (5)  k1  − ln Lγ where k1 = Band-specific thermal conversion constant (K1_CONSTANT_BAND_10) and k2 = Band-specific thermal conversion constant (K2_CONSTANT_BAND_10), we can find the constant from the image header file. Thereafter, the Surface emissivity (ε) was calculated using the proportion of vegetation coverage (PV). The following two equations were used:

 SAVI SAVI_min  Pv = square − (6) SAVImax SAVI_min − Surface emissivity ε = 0.004 Pv + 0.986, where 0.004 and 0.986 are the correlation values of ∗ surface emissivity. After calculating the brightness temperature and surface emissivity, the LST was calculated by using the following equation:    Bt  LST =   Ln(ε) (7) 1 + 0.00115 Bt  ∗ ∗ 1.4388 where 0.00115 and 1.4388 are the correlation values of LST.

3.4. Statistical Analysis To find out the significance of changing urban green cover and the impact on urban microclimate, we developed a regression model. Regression analysis is a type of predictive modeling technique that investigates the relationship between a dependent (target) and independent variable (s) (predictor) [66]. Land 2020, 9, x FOR PEER REVIEW 9 of 21

Landwhere2020, 9α,o 292, β, x, and e represent the constant, coefficient, variables, and significant error, respectively.9 of 21

4. Results It expressesThe widely the strength employed of the Spectral impact Indices of multiple is a reliable independent indicator variables for closely on observing a dependent Earth variable surfaces [67 ]. Inusing this study, remote “Stepwise sensing Regression”technology. The model EBBI was is used.the built-up The selection and bare of multipleland index independent that applies variables near- wasinfrared done with (NIR), the short help wave of an infrared automatic (SWIR), process and without thermal humaninfrared intervention. (TIR) channels This simultaneously technique basically [34]. fitsVegetation the regression has a modelhigh reflectance by adding in/ droppingNIR, but the co-variates, reflectance one of built-up at a time, or basedbare land on is a specifiedlow. In contrast, criterion. InTIR this has study, high LST reflectance was the dependentto detect built-up variable; areas while asEBBI, compared MNDWI, to vegetated and SAVI areas, were thus the independentmaking it variables.effective The for built-up aim of this areas modeling mapping was [68]. to investigate the significance of the presence of built-up area, water bodies, and vegetation on LST in the urban area. Equation (8) illustrates the regression model. 4.1. Comparative Analyses of Various Indices LST = αo + β1x1 + β2x2 + β3x3 + e (8) Spectral Indices utilize spectral reflectance properties to distinguish different LULC classes present in the study area. EBBI, MNDWI, and SAVI indices as discussed in Section 3.3 were calculated where α , β, x, and e represent the constant, coefficient, variables, and significant error, respectively. using oLandsat-8 data. Later, a non-parametric support vector machine (SVM) classifier was used to 4.extract Results LULC to validate the findings of indices. Figure 4 shows the EBBI, MNDWI, and SAVI indices for both Panaji and Tumkur cities. TheVisual widely interpretation employed Spectral of extracted Indices spectral is a reliable indices indicator for EBBI for closelyis useful observing to recognise Earth surfaceslower usingreflectance remote sensingin Panaji technology. (Figure 4a) The than EBBI Tumkur is the built-up(Figure and4b). bareIt can, land therefore, index that be appliesinferred near-infrared from the (NIR),images short that wave EBBI infrared is high for (SWIR), the Tumkur and thermal area, indi infraredcating (TIR) that channelsthe percentage simultaneously of the built-up [34]. and Vegetation bare hasland a high area reflectance is more than in NIR,twice but that the ofreflectance Panaji. The of MNDWI built-up also or baredepicts land a ismuch low. higher In contrast, waterbody TIR has highcoverage reflectance area toin detectPanaji built-up(Figure areas4c) than as comparedin Tumkur to (Figure vegetated 4d). areas, Most thusof the making area in it ePanajiffective is for built-upsurrounded areas mappingby water bodies [68]. compared to that of Tumkur (Figure 4c). Similarly, the SAVI is quite high for Panaji (Figure 4e) compared to Tumkur (Figure 4f). In Panaji, the vegetation is distributed 4.1.evenly Comparative within the Analyses city area. of Various Besides, Indices a higher reflectance value of SAVI is witnessed in the Panaji area than in Tumkur. Panaji city has much more evenly spread green areas than can be visualized for Spectral Indices utilize spectral reflectance properties to distinguish different LULC classes present Tumkur city, where green areas are distributed rather sparsely, unevenly, and mostly in the in the study area. EBBI, MNDWI, and SAVI indices as discussed in Section 3.3 were calculated using peripheral parts of the city. A clear gradient from the city core to the peripheral area is discernible Landsat-8 data. Later, a non-parametric support vector machine (SVM) classifier was used to extract (Figure 4f). There is a tradeoff between urban ecological features (green spaces and water bodies) and LULCbuilt-up to validate areas. Therefore, the findings Tumkur of indices. city needs Figure a 4better shows urban the EBBI,green MNDWI,space management and SAVI plan indices for forsmart both Panajicity development and Tumkur to cities. improve its ecosystem services.

Figure 4. Images of the Enhanced Built-Up and Bareness Index (EBBI) for Panaji (a) and Tumkur (b); the Modified Normalized Difference Water Index (MNDWI) for Panaji (c) and Tumkur (d) and the Soil Adjusted Vegetation Index (SAVI) for Panaji (e) and Tumkur (f). Land 2020, 9, 292 10 of 21

Land 2020Visual, 9, x interpretationFOR PEER REVIEW of extracted spectral indices for EBBI is useful to recognise lower reflectance10 of 21 in Panaji (Figure4a) than Tumkur (Figure4b). It can, therefore, be inferred from the images that EBBIFigure is high 4. forImages the Tumkurof the Enhanced area, indicating Built-Up and that Bareness the percentage Index (EBBI) of the for built-upPanaji (a) and and bareTumkur land (b area); is morethe than Modified twice thatNormalized of Panaji. Difference The MNDWI Water Index also (MNDWI) depicts a for much Panaji higher (c) and waterbody Tumkur (d coverage) and the area in PanajiSoil Adjusted (Figure4 Vegetationc) than in Index Tumkur (SAVI) (Figure for Panaji4d). Most(e) and of Tumkur the area (f). in Panaji is surrounded by water bodies compared to that of Tumkur (Figure4c). Similarly, the SAVI is quite high for Panaji (Figure4e) 4.2.compared Land Use to Land Tumkur Cover (Figure (LULC)4f). In Panaji, the vegetation is distributed evenly within the city area. Besides,To aexamine higher reflectancethe spatial value pattern of SAVI of LULC is witnessed in Panaji in the and Panaji Tumkur, area than SVM in Tumkur.classification Panaji was city performedhas much more in ENVI evenly for spread2019. The green LULC areas map than was can cross-checked be visualized with for Tumkur recent city, where images. green A areastotal ofare 179and distributed 251 training rather samples sparsely, were unevenly, taken for and Pana mostlyji and in Tumkur, the peripheral respectively. parts For of thethe city.validation A clear of LULCgradient map, from 120 the and city 95 core samples to the were peripheral used to area validate is discernible the results (Figure in Panaji4f). There and Tumkur, is a tradeo respectively.ff between Theurban classified ecological images features showed (green an overall spaces accuracy and water of bodies)90.31% and and a built-up Kappa coefficient areas. Therefore, of 0.83 for Tumkur Panaji andcity overall needs a accuracy better urban of 89.78% green with space Kappa management coefficient plan of 0.79 for for smart Tumkur city development city. Figure 5 toillustrates improve the its LULCecosystem pattern services. of the Panaji and Tumkur cities. An accuracy assessment of classified maps was done using4.2. Land a confusion Use Land Covermatrix. (LULC) Appendix A illustrate the overall accuracy of LULC maps and Kappa coefficient based on the confusion matrix. In Tumkur, the built-up class was the most dominant land coverTo type examine in 2019, the representing spatial pattern 41% of LULCof the intotal Panaji area and; followed Tumkur, by SVM bareclassification land (33%), vegetation was performed (30%), in andENVI water for 2019. bodies The (1%). LULC In map Panaji, was cross-checked32% of the land with area recent is Googlecovered images. with water A total bodies of 179 andclass; 251 followed training bysamples built-up were (32%), taken vegetation for Panaji and (24%), Tumkur, and respectively.bare land (12.49%). For the validation Table 3 shows of LULC the map, area 120 of various and 95 samples LULC wereclasses used in tothe validate study thearea. results Most in Panajiof the and area Tumkur, of Panaji respectively. is covered The by classified water imagesbodies showedand vegetation, an overall whereasaccuracy ofTumkur 90.31% is and covered a Kappa by coefficient built-up ofand 0.83 bare for la Panajind. Therefore, and overall the accuracy functional of 89.78% characteristic with Kappa of LULCcoefficient is completely of 0.79 for opposite Tumkur city.for the Figure two 5cities. illustrates Tumk theur LULCis dominated pattern by of the LULC Panaji of and urban Tumkur activities, cities. Anwhile accuracy Panaji assessmentis dominated of by classified urban ecological maps was features. done using a confusion matrix. AppendixA illustrate the overall accuracy of LULC maps and Kappa coefficient based on the confusion matrix. In Tumkur, the built-upTable 3. class Share was of thesatellite-derived most dominant land land use/land cover typecover in (LULC) 2019, representing for Panaji and 41% Tumkur of the totalareas area; covered followed by barefor landthis study. (33%), vegetation (30%), and water bodies (1%). In Panaji, 32% of the land area is covered with water bodies class; followed by built-up (32%), vegetation (24%), and bare land (12.49%). Table3 LULC Panaji Area in Km2 Tumkur Area in Km2 shows the area of various LULC classes in the study area. Most of the area of Panaji is covered by water Built-up area 31.65 32% 40.01 41% bodies and vegetation, whereasBare land Tumkur is covered 12.49 by built-up 13% and 32.20 bare land. 33% Therefore, the functional characteristic of LULC isWater completely bodies opposite for 30.17 the two cities. 31% Tumkur 0.97 is dominated 1% by the LULC of urban activities, while PanajiVegetation/agriculture is dominated by urban 23.78 ecological features. 24% 24.4 25%

FigureFigure 5. LandLand use/land use/land cover cover profile profile of Panaji (a) and Tumkur (b).

4.3. Land Surface Temperature (LST) The LST estimated by using the radiative transfer equation algorithm using TIR-band is effective for deducing surface emissivity in the urban landscape [6]. This technique is used to recognize the response of different thermal properties on the Earth’s surface. The response of such thermal properties varies with different landscape patterns, different geographical locations, and climatic Land 2020, 9, 292 11 of 21

Table 3. Share of satellite-derived land use/land cover (LULC) for Panaji and Tumkur areas covered for this study.

LULC Panaji Area in Km2 Tumkur Area in Km2 Built-up area 31.65 32% 40.01 41% Bare land 12.49 13% 32.20 33% Water bodies 30.17 31% 0.97 1% Vegetation/agriculture 23.78 24% 24.4 25%

4.3. Land Surface Temperature (LST) The LST estimated by using the radiative transfer equation algorithm using TIR-band is effective for deducing surface emissivity in the urban landscape [6]. This technique is used to recognize the response of different thermal properties on the Earth’s surface. The response of such thermal Land 2020, 9, x FOR PEER REVIEW 11 of 21 properties varies with different landscape patterns, different geographical locations, and climatic conditions suchsuch as rainfall,as rainfall, wind speed,wind etc. speed, Also, theetc. land Also, cover dynamicsthe land viz, cover agriculture dynamics/vegetation, viz, waterbody,agriculture/vegetation, built-up area, waterbody, bare land, built-up etc. seriously area, ba affreect land, the LSTetc. seriously variations af [6fect]. The the LSTLST imagesvariations for [6].Panaji The and LST Tumkur images for (Figure Panaji6a,b) and indicate Tumkur a (Figure temperature 6a,b) indicate range of a 34–38temperature◦C in range most areaof 34–38 of Panaji °C in mostand 42–46 area of◦C Panaji for the and most 42–46 area °C offor Tumkur the most city area The of Tumkur LST pattern city The in Panaji LST pattern is largely in Panaji homogeneous, is largely homogeneous,unlike the quite unlike heterogeneous the quite oneheterogeneous of Tumkur. one In Panaji, of Tumkur. about 35%In Panaji, of the about area is 35% covered of the by area water is coveredbodies and by water experiences bodiestemperatures and experiences< 30 temperatures◦C. In Tumkur, < 30 only °C. In a fewTumkur, places only close a few to water places bodies close and vegetation experience < 30 ◦C. The maximum LST of ( ) 46 ◦C is observed in approximately 4% to water bodies and vegetation experience < 30 °C. The maximum≥ LST of (≥) 46 °C is observed in approximatelyof the land area 4% of of Tumkur the land city. area On of the Tumkur other hand,city. On in the Panaji, other the hand, LST in maximum Panaji, the of LST 42 ◦ Cmaximum to 46 ◦C ofis in42< °C1% to of46 the °C landis in < area. 1% of The the maximum land area. and The minimum maximum LST and di minimumfferences areLST lower differences in Panaji are (12 lower◦C) inand Panaji higher (12 in °C) Tumkur and higher (16 ◦C). in Tumkur (16 °C).

FigureFigure 6. 6. LandLand Surface Surface Temperature Temperature profile profile of Panaji ( a) and Tumkur ((b).).

4.4. Relationship Relationship of LST and EBBI, MNDWI, and SAVI Figure 77 illustratesillustrates thethe non-parametricnon-parametric testtest withwith indicesindices ofof urbanurban featuresfeatures and LST. Generally,Generally, LST bears bears a a negative negative relationship relationship with with SAVI SAVI and and MNDWI, MNDWI, and and a positive a positive relationship relationship with with EBBI. EBBI. The The 30 m 30 m pixel-based correlation coefficient values between LST and SAVI, MNDWI and EBBI 30 m × 30× m pixel-based correlation coefficient values between LST and SAVI, MNDWI and EBBI were significantsignificant (P < 0.01). The The EBBI EBBI had had a a strong positive correlation with LST among these three indices for both cities (Figure 77a,d).a,d). ThisThis impliesimplies thatthat bothboth barebare landland andand built-upbuilt-up areare causescauses ofof highhigh LST in both cities. The SAVI and MNDWI indicate a strong negative correlation with LST for both cities (Figure 7b,c,e,f). A weak correlation of −0.37 between LST and SAVI is seen for Panaji compared to Tumkur with a strong correlation of −0.75. This may be due to a low UHI effect in Panaji compared to Tumkur. Besides, Panaji city being located near the seashore and also interspersed by water channels, experiences minimal LST differences. Land 2020, 9, 292 12 of 21

LST in both cities. The SAVI and MNDWI indicate a strong negative correlation with LST for both cities (Figure7b,c,e,f). A weak correlation of 0.37 between LST and SAVI is seen for Panaji compared to − Tumkur with a strong correlation of 0.75. This may be due to a low UHI effect in Panaji compared to − Tumkur. Besides, Panaji city being located near the seashore and also interspersed by water channels, experiences minimal LST differences. Land 2020, 9, x FOR PEER REVIEW 12 of 21

FigureFigure 7. 7. CorrelationCorrelation coecoefficientfficient values between between LS LSTT (dependent (dependent variable) variable) and and other other indices indices (independent variables) obtained through the analyses of data from Landsat OLI for Panaji (a), (b), (independent variables) obtained through the analyses of data from Landsat OLI for Panaji (a–c), (c) and Tumkur (d), (e), (f). and Tumkur (d–f)

4.5.4.5. Regression Regression Model Model TheThe multivariate multivariate regression regression modelmodel derivedderived in this analysis analysis is is useful useful to to denote denote the the relationship relationship betweenbetween LST LST and and EBBI, EBBI, MNDWI, MNDWI, andand SAVI.SAVI. LST being the the dependent dependent variable, variable, is isaffected affected by byEBBI, EBBI, MNDWI, and SAVI (the independent variables). The adjusted R2 in the linear regression model is 0.698 (with standard error 1.407) for Panaji city. A high 0.582 Durbin-Watson d (within the two critical values of 0 < d < 2) signifies a strong linear auto-correlation. Similar to Tumkur, the highly significant F-test (P < 0.0001) ascertains that the model explains a significant amount of the variance in LST. By subjecting all variables to multiple linear regression, it is inferred that SAVI, MNDWI, and even EBBI serve as significant predictors as there was a high level of significance (P < 0.0001) between LST and Land 2020, 9, 292 13 of 21

MNDWI, and SAVI (the independent variables). The adjusted R2 in the linear regression model is 0.698 (with standard error 1.407) for Panaji city. A high 0.582 Durbin-Watson d (within the two critical values of 0 < d < 2) signifies a strong linear auto-correlation. Similar to Tumkur, the highly significant F-test (P < 0.0001) ascertains that the model explains a significant amount of the variance in LST. By subjecting all variables to multiple linear regression, it is inferred that SAVI, MNDWI, and even EBBILand 2020 serve, 9, x as FOR significant PEER REVIEW predictors as there was a high level of significance (P < 0.0001) between13 LSTof 21 and all three independent variables. The significance level for SAVI, MNDWI, and EBBI is less than 0.0001.all three We independent also observe variables. that the The impact significance of EBBI andlevel MNDWI for SAVI, is MNDWI, higher than and SAVI EBBI byis less comparing than 0.0001. the standardizedWe also observe coeffi thatcients the (beta impact coe ffiofcient EBBI for and SAVI MNDWI= 0.168, is MNDWIhigher than= 1.06SAVI and by EBBIcomparing= 0.00338). the − − Fromstandardized the multiple coefficients linear regression (beta coefficient model, the for following SAVI = − relationship0.168, MNDWI is predicted = −1.06 amongand EBBI LST = and0.00338). EBBI, MNDWI,From the andmultiple SAVI linear for Panaji regression city with model, unstandardized the following coe relationshipfficient of the is variables: predicted among LST and EBBI, MNDWI, and SAVI for Panaji city with unstandardized coefficient of the variables: LST (Panaji) = 114.531 + 0.563 EBBI 86.349 MNDWI 2.206 SAVI. (9) LST (Panaji) = 114.531 + 0.563 EBBI −− 86.349 MNDWI − 2.206 SAVI. (9) For TumkurTumkur city, city, the the adjusted adjusted R2 valueR2 value is 0.716 is 0.716 (with (with a standard a standard error oferror 1.97). of The1.97). Durbin-Watson The Durbin- dWatsonbeing 0.578d being brings 0.578 forth brings a strong forth linear a strong auto-correlation linear auto-correlation between di ffbetweenerent variables different used variables for multiple used linearfor multiple regression linear analyses. regression With analyses. the F-test With revealing the F-test a highly revealing significant a highly (P significant< 0.0001) relationship,(P < 0.0001) itrelationship, is confirmatory it is confirmatory that this model that explains this model a significant explains amounta significant of the amount variance of the in LSTvariance for Tumkur in LST city.for Tumkur By forcing city. all By variables forcing all into variables the multiple into the linear multiple regression, linear itregression, can be discerned it can be that discerned both SAVI that andboth MNDWI SAVI and are MNDWI significant are predictors. significant The predictors. level of significance The level of for significance SAVIand MNDWI for SAVI is aand high MNDWI of <0.001, is whilea high for of EBBI,<0.001, it iswhile a low for of 0.058.EBBI, Weit is also a low observe of 0.058. that We the impactalso observe of SAVI that and the MNDWI impact is of higher SAVI thanand EBBIMNDWI by comparing is higher than the standardized EBBI by comparing coefficients the standa (beta coerdizedfficient coefficients for SAVI =(beta0.483, coefficient MNDWI for= SAVI0.439 = − − and−0.483, EBBI MNDWI= 0.016). = −0.439 From and this EBBI multiple = 0.016). linear From regression this multiple model, linear the regression following model, relationship the following among LSTrelationship and EBBI, among MNDWI, LST andand SAVI EBBI, is predictedMNDWI, forand Tumkur SAVI is city predicted with an unstandardizedfor Tumkur city coe withfficient an ofunstandardized variables. coefficient of variables. LST (Tumkur) = 97.80 + 0.036 EBBI 62.987 MNDWI 8.272 SAVI (10) LST (Tumkur) = 97.80 + 0.036 EBBI −− 62.987 MNDWI − 8.272 SAVI (10) From checkingchecking forfor normality normality of of residuals residuals with with a normala normal P-P P-P plot plot (Figure (Figure8), it 8), can it becan inferred be inferred that thethat points the points generally generally follow follow the normal the normal diagonal diagonal line with line no with strong no deviations,strong deviations, indicating indicating a normal a distributionnormal distribution of residuals of residuals for both cities.for both Apparently, cities. Apparently, the regression the regression model developed model developed in this analysis in this is applicableanalysis is onlyapplicable for these only cities. for these Owing cities. to geomorphological Owing to geomorphological heterogeneity heterogeneity of different cities, of different future studiescities, future can consider studies inputscan consider of land inputs cover of variations land cover using variations this strategy. using this strategy.

Figure 8.8. ExpectedExpected cumulativecumulative probability vs.vs. Observed cumulative probability plots (P-P plot) forfor Panaji ((aa)) andand TumkurTumkur ((bb).).

5. Discussion This comparative study focused on spectral indices and their influence on LST in the coastal city of Panaji and the interior city of Tumkur slated for development as smart cities. Low urban landscape patterns in Panaji are mixed with built-up areas and vegetation. There is a possibility of confusion towards extracting built-up areas using EBBI approach. Ahmed et al., [69] reported that the tremendous pressure of urbanization on the peripheral area of a city leads to the conversion of non- built-up areas (green area, agriculture land, waterbody) into built-up areas. The first step of such a conversion process is ecosystem sensitive land cover to bare land and finally, it is replaced by built- Land 2020, 9, 292 14 of 21

5. Discussion This comparative study focused on spectral indices and their influence on LST in the coastal city of Panaji and the interior city of Tumkur slated for development as smart cities. Low urban landscape patterns in Panaji are mixed with built-up areas and vegetation. There is a possibility of confusion towards extracting built-up areas using EBBI approach. Ahmed et al., [69] reported that the tremendous pressure of urbanization on the peripheral area of a city leads to the conversion of non-built-up areas (green area, agriculture land, waterbody) into built-up areas. The first step of such a conversion process is ecosystem sensitive land cover to bare land and finally, it is replaced by built-up area [70]. In this context, Tumkur is environmentally more vulnerable than Panaji due to the presence of more bare land. MNDWI can extract the water information efficiently from both built-up and non-built-up areas. It can dampen the built-up land information effectively while highlighting water information and can accurately extract the water bodies information [33]. However, from a statistical perspective, when most land cover types differ from each other, the MNDWI’s efficiency in identifying water, saturated soil, or different water content of the vegetation can lead to misclassification. Biggs et al., [71] emphasized that the presence of water bodies (ponds, small lakes, low-order streams, ditches, or springs) are critical for freshwater biodiversity and are increasingly recognised for their role in a variety of ecosystem services as well as the aquatic ecosystem. Jackson et al., [72] reported that MNDWI is positively correlated with vegetation water content and useful for assessing the extent of strains of drought in any area. In Tumkur, where the availability of water bodies is minimal, it can be suggested that the functioning of services is negatively affected, and vegetative areas might experience water stress during summer. The high value of MNDWI in Panaji is related to high vegetation water content and it might help vegetation to have less water stress during summer. SAVI is the measuring index for vegetation or green spaces which has more functional value for the urban ecosystem. The significant coefficient of the regression model illustrates that MNDWI and SAVI are accountable for combatting LST. Although the percent share of SAVI and MNDWI is higher than that of built-up and bare land, MNDWI was the most significant predictor for controlling LST in Panaji, while SAVI was the important predictor for Tumkur. Surface temperature also has a direct interaction with LULC characteristics [73]. Therefore, the analysis of the relationship between LULC and LST is crucial in order to understand the effects of LULC on UHI. The land cover dynamics are accountable for elevated or decreased LST. Higher/significant correlation coefficient values between LST and EBBI, MNDWI, and SAVI for Panaji and Tumkur cities clearly reflect that both bare land and built-up area cause, accelerate, and sustain higher LSTs. It can be inferred that the existing coverage of water bodies in Panaji seems to dampen the LST to a greater extent when compared to Tumkur. Both LULC and spectral indices, as well as the observed higher LST in Tumkur, signify inefficient ecosystem services more than a better service could in Panaji city. With much lower vegetation cover in Tumkur, the degree of absorption of solar radiation by built-up and dry soil drastically elevates the LST and alters the conditions of the near-surface atmosphere [74] over Tumkur city. Tran et al., (2017) [8] inferred that absolute LST is useful in characterizing UHIs on a short-term basis (particular date) but is not effective in comparing spatial patterns of UHI through time. To ensure that LST values retrieved from different images are comparable, Walawender et al., (2014) [75] had proposed the use of normalized LST to investigate the LST spatial distribution in relation to LULC. Many earlier studies [76–78] had emphasized that the simulation of future LST, based on LULC, helps in mitigating UHIs effects and in adopting new strategies and in land use planning and to reduce/control the UHIs effect. In view of such possibilities, it can be inferred from our analyses that both the cities are prone to increased LST for reasons mentioned earlier. Further, as Guo et al., (2015 [79] and 2016 [80]) recommend, these cities would be better placed for LST prediction by taking into account the complex landscape structure and heterogeneity. Results of this study showcase the effectiveness of the spectral indices derived in identifying the LST differences Land 2020, 9, 292 15 of 21 within the study area and in marking UHIs. These could provide crucial feedback to planners and policy-makers for the inclusion of UHIs mitigation measures. In general, LST is negatively correlated with SAVI and MNDWI and positively correlated with EBBI. But, these relationships tend to change with UHI [70]. With both SAVI and MNDWI bearing a stronger correlation with LST in both the cities, it can be suggested that the impact of UHI is strong in at least over 50% of the areas. Guha et al. [81] reported that vegetation areas have a much better negative correlation with LST, but these relationships gradually weaken with the increase of the heterogeneous surface features. Although Panaji has a smaller urban area compared to Tumkur, the large water body, forest cover, and hilly features render Panaji city as a more heterogeneous surface than Tumkur city, which is located in the plains of India’s interior. A weak correlation (R2 = 0.37) in Panaji city and a − significant correlation (R2 = 0.75) between LST and SAVI in Tumkur city can be taken to suggest that − there is a lower UHI effect in Panaji city than in Tumkur city, a fact that can be confirmed through visual analyses of the images (Figure5). In Panaji city located near the sea, the water channels surrounding the city area are minimizing the LST differences. Though there is more green cover in Panaji, it is inadequate according to the Urban and Regional Development Plans Formulation & Implementation (URDPFI) guidelines [54]. Currently, existing roadside plants and 17 parks are irrigated by the Public Works Department (PWD) by diverting an unspecified volume of urban drinking water supplies. While over 70% of domestic sewage is claimed to be treated, all the treated water from Tonca sewage treatment plant (STP) is drained into an adjacent polluted creek ‘to improve its water quality’. In Tumkur, under the Smart City Initiative [82], there are plans to increase the number(/area) of green spaces. Unlike in Panaji where the monsoonal rains help sustain green spaces for quite long periods (with once a fortnight watering), Tumkur, located in the interior region, receives less rainfall. Thus, a 150 km-long canal is planned for potable water supply from River Hemavathi to this rapidly urbanizing, water-scarce city. Besides meeting drinking water demand, the canal is expected to help meet water requirements for existing and planned parks and roadside vegetation. To provide respite from unhealthy LST and UHI impacts, maintenance of existing parks, adding many more new ones, and roadside trees to increase the green cover are vital steps. The fact that urban greening would help overcome the adverse effects of elevated LST [83–85] ought to sensitize the development of both Panaji and Tumkur as smart cities. Further, as Thapa and Murayama [86], and Ramaiah and Avtar [87] suggest, green areas must be considered. As reported from Dhaka [88], with the percent share of urban green areas getting reduced due to unplanned urban activities and weak land-use regulations, Tumkur city needs a sound urban green space management strategy. While some alternative options exist for surface water (such as groundwater, water supply from outside the urban areas, treated urban wastewater), there are no alternatives for green spaces and their services. Green spaces not only control LST, they also provide recreational facilities, beautify the city, contribute to atmospheric oxygen, and purify the air for maintaining ecological balance and for the upkeep of biodiversity [89]. From a significant positive spatial autocorrelation observed between LST and greenspace area within the chosen section/s of expansive Beijing, Li et al., (2012) [90] suggested that green spaces bring down summertime LST by 5 ◦C. In Tumkur city in particular, UGS can reduce temperatures through direct shading and evapotranspiration, and as Tyrväinen et al., (2005) [91] recommended, they can create a local cool island within this sprawling urban area. With proper maintenance practices, including the use of treated wastewater, the existing vegetation in its 400 plus listed parks in addition to new ones (including roadside plantation) can also achieve many other environmental benefits such as reduced rainwater runoff, greater urban biodiversity, and improved aesthetics [92,93]. Although there may not be a linear relationship between the cooling effect and the size of greenspace [94], there would be discernible cooling effects in specific locations with denser vegetation. These desirable services are necessary for a high-quality living environment. The use of advanced remote sensing techniques can help in the development and maintenance of UGS as part of the smart city programs and implementation of SDGs 11. Land 2020, 9, 292 16 of 21

6. Conclusions Any existing urban fabric would lead to rapid changes in the urban environment whenever altered. This, in turn leads to deviations in urban settings in a variety of ways. Thus, urban settlements ought to plan and create facilities/amenities that are ergonomic, long-lasting, and encompassing. This study compares two upcoming smart cities (Panaji and Tumkur) in India using landscape sensitivity analysis and its influence on LST. The urban factors such as EBBI, SAVI, and MNDWI influence the LST in both cities. The study reveals that water bodies and green spaces are actively responsible for dampening LST. The performances of LST dampening depends on the maximum share of cooling surfaces (water bodies and green spaces) in the study area. In the Panaji, the correlation coefficient between EBBI, SAVI, and MNDWI with LST is about 0.72, 0.37, and 0.83, respectively. On the other hand, in the − − Tumkur, the correlation coefficient between EBBI, SAVI, and MNDWI with LST is about 0.829, 0.77, − and 0.753, respectively. The multivariate regression model reveals that in the Tumkur, the adjusted − R2 of the developed model is 0.716 with the standard error 1.97 and in the Panaji, the adjusted R2 of the developed model is 0.698 with the standard error being 1.407. This study did not consider the local climate issues such as wind speed, rainfall, topography, and functional activities of the urban area (e.g., industrial and economic activities, building densities, transportation) may reinforce the findings. Therefore, there is a need to consider these limitations open for further investigations. The use of advanced remote sensing techniques can help to maintain urban green spaces for healthy and livable city development. The analyses of this study would serve as guidelines for an in-depth investigation of the significance of urban green spaces in controlling/reducing urban heat island effects.

Author Contributions: Conceptualization, M.R., R.A., M.M.R.; methodology, M.R., M.M.R.; formal analysis, M.R., M.M.R., R.A.; investigation, M.R.; writing M.R., M.M.R., R.A.; writing—review and editing, M.R., R.A. All authors have read and agreed to the published version of the manuscript. Funding: No external funding was obtained beyond that discussed below. Acknowledgments: The first author is grateful to the Faculty of Environmental Earth Science, Hokkaido University for facilities and JASSO for the scholarship support. We are thankful to the United States Geological Survey (USGS) for providing Landsat satellite data and the Global Challenge Research Funds (GCRF) of the University of . The authors acknowledge the support of the urban development departments of Panaji and Tumkur cities for providing necessary data and information. Constructive suggestions offered by anonymous reviewers helped improve and revise the manuscript. Conflicts of Interest: The authors declare no conflict of interest.

Appendix A Tables A1 and A2 show the results of the accuracy assessment of LULC classification based on Landsat-8 data acquired in 2019 for Panaji and Tumkur.

Table A1. Confusion matrix of the 2019 LULC map of Panaji.

Built-Up Area Bare Land Water Body Vegetation/Agriculture Classification Overall Producer’s Accuracy Built-up area 20 3 0 0 23 86.95% Bare land 2 18 0 0 20 90.00% Water body 0 0 24 3 27 88.89% Vegetation/agriculture 0 0 2 23 25 92.00% Overall Truth 22 21 26 26 95 User’s accuracy 90.90% 86.72% 90.30% 88.46% Overall Accuracy 90.32% Kappa coefficient 0.83 Land 2020, 9, 292 17 of 21

Table A2. Confusion matrix of the 2019 LULC map of Tumkur.

Built-Up Area Bare Land Water Body Vegetation/Agriculture Classification Overall Producer’s Accuracy Built-up area 25 5 0 0 30 83.33% Bare land 4 23 0 0 27 85.18% Water body 0 0 27 5 32 84.37% Vegetation/agriculture 0 0 6 25 31 80.64% Overall Truth 29 28 33 30 120 User’s accuracy 86.21% 82.14% 81.81% 83.33% Overall Accuracy 83.33% Kappa coefficient 0.778

References

1. Koomen, E.; Rietveld, P.; Bação, F. The third dimension in : The urban-volume approach. Environ. Plan. B Plan. Des. 2009, 36, 1008–1025. [CrossRef] 2. Estoque, R.; Murayama, Y.; Tadono, T.; Thapa, R. Measuring urban volume: Geospatial technique and application. Tsukuba Geoenviron. Sci. 2015, 11, 13–20. 3. Wheeler, S. Planning for Sustainability; Routledge: Abington, UK, 2004; ISBN 0203300564. 4. Hua, A.K.; Ping, O.W. The influence of land-use/land-cover changes on land surface temperature: A case study of Kuala Lumpur metropolitan city. Eur. J. Remote Sens. 2018, 51, 1049–1069. [CrossRef] 5. Amiri, R.; Weng, Q.; Alimohammadi, A.; Alavipanah, S.K. Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sens. Environ. 2009, 113, 2606–2617. [CrossRef] 6. Rahman, M.; Avtar, R.; Yunus, A.P.; Dou, J.; Misra, P.; Takeuchi, W.; Sahu, N.; Kumar, P.; Johnson, B.; Dasgupta, R.; et al. Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka. Remote Sens. 2020, 12, 1191. [CrossRef] 7. Naserikia, M.; Asadi Shamsabadi, E.; Rafieian, M.; Filho, W. The Urban Heat Island in an Urban Context: A Case Study of Mashhad, Iran. Int. J. Environ. Res. Public Health 2019, 16, 313. [CrossRef][PubMed] 8. Tran, D.X.; Pla, F.; Latorre-Carmona, P.; Myint, S.W.; Caetano, M.; Kieu, H.V. Characterizing the relationship between land use land cover change and land surface temperature. ISPRS J. Photogramm. Remote Sens. 2017, 124, 119–132. [CrossRef] 9. Liu, W.; Ji, C.; Zhong, J.; Jiang, X.; Zheng, Z. Temporal characteristics of the Beijing urban heat island. Theor. Appl. Climatol. 2007, 87, 213–221. [CrossRef] 10. Xiao, R.; Ouyang, Z.-Y.; Zheng, H.; Li, W.-F.; Schienke, E.W.; Wang, X.-K. Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. J. Environ. Sci. 2007, 19, 250–256. [CrossRef] 11. Xiao, R.; Weng, Q.; Ouyang, Z.; Li, W.; Schienke, E.W.; Zhang, Z. Land surface temperature variation and major factors in Beijing, China. Photogramm. Eng. Remote Sens. 2008, 74, 451–461. [CrossRef] 12. Weng, Q.; Lu, D.; Schubring, J. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 2004, 89, 467–483. [CrossRef] 13. Yang, L.; Xian, G.; Klaver, J.; Deal, B. Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data. Photogramm. Eng. Remote Sens. 2003, 69, 1003–1010. [CrossRef] 14. Rizwan, A.M.; Dennis, L.Y.; Chunho, L. A review on the generation, determination and mitigation of Urban Heat Island. J. Environ. Sci. 2008, 20, 120–128. [CrossRef] 15. Plocoste, T.; Jacoby-Koaly, S.; Molinié, J.; Petit, R.H. Evidence of the effect of an urban heat island on air quality near a landfill. Urban Clim. 2014, 10, 745–757. [CrossRef] 16. Alghannam, A.R.O.; Al-Qahtnai, M.R.A. Impact of vegetation cover on urban and rural areas of arid climates. Aust. J. Agric. Eng. 2012, 3, 1. 17. Ng, E.; Chen, L.; Wang, Y.; Yuan, C. A study on the cooling effects of greening in a high-density city: An experience from Hong Kong. Build. Environ. 2012, 47, 256–271. [CrossRef] 18. Grover, A.; Babu Singh, R. Analysis of Urban Heat Island (UHI) in Relation to Normalized Difference Vegetation Index (NDVI): A Comparative Study of Delhi and Mumbai. Environments 2015, 2, 125–138. [CrossRef] Land 2020, 9, 292 18 of 21

19. Avtar, R.; Tripathi, S.; Aggarwal, A.K. Assessment of Energy–Population–Urbanization Nexus with Changing Energy Industry Scenario in India. Land 2019, 8, 124. [CrossRef] 20. Lilly Rose, A.; Devadas, M.D. Analysis of Land Surface Temperature and Land Use/Land Cover Types Using Remote Sensing Imagery-A Case in City, India. In Proceedings of the 7th International Conference on Urban Climate (ICUC-7), Yokohama, Japan, 29 June–3 July 2009; Volume 29. 21. Weng, Q.; Liu, H.; Lu, D. Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis, United States. Urban Ecosyst. 2007, 10, 203–219. [CrossRef] 22. Sannigrahi, S.; Bhatt, S.; Rahmat, S.; Uniyal, B.; Banerjee, S.; Chakraborti, S.; Jha, S.; Lahiri, S.; Santra, K.; Bhatt, A. Analyzing the role of biophysical compositions in minimizing urban land surface temperature and urban heating. Urban Clim. 2018, 24, 803–819. [CrossRef] 23. Sinha, S.; Pandey, P.C.; Sharma, L.K.; Nathawat, M.S.; Kumar, P.; Kanga, S. Remote estimation of land surface temperature for different LULC features of a moist deciduous tropical forest region. In Remote Sensing Applications in Environmental Research; Springer: Basel, Switzerland, 2014; pp. 57–68. 24. Feizizadeh, B.; Blaschke, T. Examining urban heat island relations to land use and : Multiple endmember spectral mixture analysis for thermal remote sensing. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 1749–1756. [CrossRef] 25. USEPA Heat Island Impacts: Compromised Human Health and Comfort. Available online: https://www.epa. gov/heat-islands/heat-island-impacts (accessed on 6 June 2020). 26. Yu, Z.; Guo, X.; Zeng, Y.; Koga, M.; Vejre, H. Variations in land surface temperature and cooling efficiency of green space in rapid urbanization: The case of Fuzhou city, China. Urban For. Urban Green. 2018, 29, 113–121. [CrossRef] 27. Du, H.; Cai, W.; Xu, Y.; Wang, Z.; Wang, Y.; Cai, Y. Quantifying the cool island effects of urban green spaces using remote sensing Data. Urban For. Urban Green. 2017, 27, 24–31. [CrossRef] 28. Imhoff, M.L.; Zhang, P.; Wolfe, R.E.; Bounoua, L. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sens. Environ. 2010, 114, 504–513. [CrossRef] 29. Nichol, J.E.; To, P.H. Temporal characteristics of thermal satellite images for urban heat stress and heat island mapping. ISPRS J. Photogramm. Remote Sens. 2012, 74, 153–162. [CrossRef] 30. Weng, Q. A remote sensing? GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. Int. J. Remote Sens. 2001, 22, 1999–2014. 31. Bonafoni, S.; Keeratikasikorn, C. Land Surface Temperature and Urban Density: Multiyear Modeling and Relationship Analysis Using MODIS and Landsat Data. Remote Sens. 2018, 10, 1471. [CrossRef] 32. Avtar, R.; Kumar, P.; Oono, A.; Saraswat, C.; Dorji, S.; Hlaing, Z. Potential application of remote sensing in monitoring ecosystem services of forests, mangroves and urban areas. Geocarto Int. 2017, 32, 874–885. [CrossRef] 33. Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006, 27, 3025–3033. [CrossRef] 34. As-syakur, A.; Adnyana, I.; Arthana, I.W.; Nuarsa, I.W. Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area. Remote Sens. 2012, 4, 2957–2970. [CrossRef] 35. Yengoh, G.T.; Dent, D.; Olsson, L.; Tengberg, A.E.; Tucker, C.J., III. Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations; Springer: Berlin, Germany, 2015; ISBN 3319241125. 36. Saini, V.; Arora, M.K.; Gupta, R.P. Relationship between surface temperature and SAVI using Landsat data in a coal mining area in India. In Proceedings of the Land Surface and Cryosphere Remote Sensing III, New Delhi, India, 4–7 April 2016; Volume 9877, p. 987711. 37. Eswar, R.; Sekhar, M.; Bhattacharya, B.K. Disaggregation of LST over India: Comparative analysis of different vegetation indices. Int. J. Remote Sens. 2016, 37, 1035–1054. [CrossRef] 38. McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425–1432. [CrossRef] 39. Gascon, M.; Cirach, M.; Martínez, D.; Dadvand, P.; Valentín, A.; Plasència, A.; Nieuwenhuijsen, M.J. Normalized difference vegetation index (NDVI) as a marker of surrounding greenness in epidemiological studies: The case of city. Urban For. Urban Green. 2016, 19, 88–94. [CrossRef] 40. Gill, S.; Handley, J.F.; Ennos, R.; Pauleit, S. Adapting Cities for Climate Change: The Role of the Green Infrastructure. Built Environ. 2007, 33, 115–133. [CrossRef] Land 2020, 9, 292 19 of 21

41. Hulme, M.; Turnpenny, J.; Jenkins, G.J. Climate Change Scenarios for the United Kingdom: The UKCIP02 Briefing Report; Tyndall Centre for Climate Change Research, School of Environmental Sciences: Norwich, UK, 2002; ISBN 0-902170-65-1. 42. Bowler, D.E.; Buyung-Ali, L.; Knight, T.M.; Pullin, A.S. Urban greening to cool towns and cities: A systematic review of the empirical evidence. Landsc. Urban Plan. 2010, 97, 147–155. [CrossRef] 43. Georgi, J.N.; Dimitriou, D. The contribution of urban green spaces to the improvement of environment in cities: Case study of Chania, Greece. Build. Environ. 2010, 45, 1401–1414. [CrossRef] 44. Hamada, S.; Ohta, T. Seasonal variations in the cooling effect of urban green areas on surrounding urban areas. Urban For. Urban Green. 2010, 9, 15–24. [CrossRef] 45. Oliveira, S.; Andrade, H.; Vaz, T. The cooling effect of green spaces as a contribution to the mitigation of urban heat: A case study in Lisbon. Build. Environ. 2011, 46, 2186–2194. [CrossRef] 46. Semrau, A. Introducing Cool Communities. Am. For. 1992, 98, 49–52. 47. Rosenfeld, A.H.; Akbari, H.; Romm, J.J.; Pomerantz, M. Cool communities: Strategies for heat island mitigation and smog reduction. Energy Build. 1998, 28, 51–62. [CrossRef] 48. Tumakuru Smart City. Available online: http://demo.oasisweb.in/smartcitytumakuru/NewTemplate/ HomeNew.aspx#projects (accessed on 1 August 2020). 49. Avtar, R.; Aggarwal, R.; Kharrazi, A.; Kumar, P.; Kurniawan, T.A. Utilizing geospatial information to implement SDGs and monitor their Progress. Environ. Monit. Assess. 2019, 192, 35. [CrossRef][PubMed] 50. Smart Cities Mission. Ministry of Urban Development; Smart Cities Mission: New Delhi, India, 2016. 51. Ministry of Urban Development. Government of India SMART CITY MISSION. Available online: https: //smartnet.niua.org/sites/default/files/resources/Panaji_SCP.pdf (accessed on 10 August 2020). 52. Panaji Climate: Average Temperature, Weather By Month, Panaji Weather Averages. Available online: https://en.climate-data.org/asia/india/goa/panaji-6394/ (accessed on 22 August 2019). 53. Tumakuru Climate: Average Temperature, Weather by Month, Tumakuru Weather Averages. Available online: https://en.climate-data.org/asia/india/karnataka/tumakuru-47643/ (accessed on 12 September 2019). 54. Corporation of the City of Panaji. CRISIL Risk and Infrastructure Solutions Limited Revised City Development Plan for Panaji, 2041. Available online: http://imaginepanaji.com/wp-content/uploads/2015/11/Revised-City- Development-Plan-for-Panaji-2041-2.pdf (accessed on 17 September 2019). 55. Prakash, Y. Smart Cities Mission in India: An Empirical study on opportunities and Challenges. In Urbanization in India: Issues and Challenges; Avni Publications: Rajasthan, India, 2017; pp. 125–134. ISBN 978-93-82968-76-4. 56. Myint, S.W.; Wentz, E.A.; Brazel, A.J.; Quattrochi, D.A. The impact of distinct anthropogenic and vegetation features on urban warming. Landsc. Ecol. 2013, 28, 959–978. [CrossRef] 57. Zhang, Y.; Chen, L.; Wang, Y.; Chen, L.; Yao, F.; Wu, P.; Wang, B.; Li, Y.; Zhou, T.; Zhang, T. Research on the contribution of urban land surface moisture to the alleviation effect of urban land surface heat based on landsat 8 data. Remote Sens. 2015, 7, 10737–10762. [CrossRef] 58. EarthExplorer. Available online: https://earthexplorer.usgs.gov/ (accessed on 10 August 2020). 59. Sonka, M.; Hlavac, V.; Boyle, R. Image pre-processing. In Image Processing, Analysis and Machine Vision; Springer: Berlin, Germany, 1993; pp. 56–111. 60. Zha, Y.; Gao, J.; Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 2003, 24, 583–594. [CrossRef] 61. Bouzekri, S.; Lasbet, A.A.; Lachehab, A. A new spectral index for extraction of built-up area using Landsat-8 data. J. Indian Soc. Remote Sens. 2015, 43, 867–873. [CrossRef] 62. He, C.; Shi, P.; Xie, D.; Zhao, Y. Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sens. Lett. 2010, 1, 213–221. [CrossRef] 63. Li, H.; Wang, C.; Zhong, C.; Su, A.; Xiong, C.; Wang, J.; Liu, J. Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index. Remote Sens. 2017, 9, 249. [CrossRef] 64. Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25, 295–309. [CrossRef] 65. Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 1994, 48, 119–126. [CrossRef] 66. Tirta, I.M.; Anggraeni, D.; Pandutama, M. Online Statistical Modeling (Regression Analysis) for Independent Responses. J. Phys. Conf. Ser. 2017, 855, 12054. [CrossRef] 67. Dobson, A.J. Introduction to Statistical Modelling; Springer: Berlin, Germany, 2013; ISBN 1489931740. Land 2020, 9, 292 20 of 21

68. Herold, M.; Gardner, M.E.; Roberts, D.A. Spectral resolution requirements for mapping urban areas. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1907–1919. [CrossRef] 69. Ahmed, B.; Hasan, R.; Maniruzzaman, K.M. Urban Morphological Change Analysis of Dhaka City, Bangladesh, Using Space Syntax. ISPRS Int. J. Geo-Inf. 2014, 3, 1412–1444. [CrossRef] 70. Alam, M.J. Rapid urbanization and changing land values in mega cities: Implications for housing development projects in Dhaka, Bangladesh. Bdg. J. Glob. South 2018, 5, 1–19. [CrossRef] 71. Biggs, J.; von Fumetti, S.; Kelly-Quinn, M. The importance of small waterbodies for biodiversity and ecosystem services: Implications for policy makers. Hydrobiologia 2016, 793, 3–39. [CrossRef] 72. Jackson, T.J.; Chen, D.; Cosh, M.; Li, F.; Anderson, M.; Walthall, C.; Doriaswamy, P.; Hunt, E.R. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sens. Environ. 2004, 92, 475–482. [CrossRef] 73. Quattrochi, D.A.; Luvall, J.C. Thermal infrared remote sensing for analysis of landscape ecological processes: Methods and applications. Landsc. Ecol. 1999, 14, 577–598. [CrossRef] 74. Mallick, J.; Kant, Y.; Bharath, B.D. Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys. Union 2008, 12, 131–140. 75. Walawender, J.P.; Szymanowski, M.; Hajto, M.J.; Bokwa, A. Land Surface Temperature Patterns in the Urban Agglomeration of Krakow (Poland) Derived from Landsat-7/ETM+ Data. Pure Appl. Geophys. 2014, 171, 913–940. [CrossRef] 76. Yuan, F.; Bauer, M.E. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens. Environ. 2007, 106, 375–386. [CrossRef] 77. Adams, M.P.; Smith, P.L. A systematic approach to model the influence of the type and density of vegetation cover on urban heat using remote sensing. Landsc. Urban Plan. 2014, 132, 47–54. [CrossRef] 78. Rotem-Mindali, O.; Michael, Y.; Helman, D.; Lensky, I.M. The role of local land-use on the urban heat island effect of Tel Aviv as assessed from satellite remote sensing. Appl. Geogr. 2015, 56, 145–153. [CrossRef] 79. Guo, G.; Wu, Z.; Xiao, R.; Chen, Y.; Liu, X.; Zhang, X. Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landsc. Urban Plan. 2015, 135, 1–10. [CrossRef] 80. Guo, G.; Zhou, X.; Wu, Z.; Xiao, R.; Chen, Y. Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou, China. Environ. Model. Softw. 2016, 84, 427–439. [CrossRef] 81. Guha, S.; Govil, H.; Gill, N.; Dey, A. Analytical study on the relationship between land surface temperature and land use/land cover indices. Ann. GIS 2020, 26, 201–216. [CrossRef] 82. Sharma, P. Tumkur: A Smart City in the Making. Available online: https://www.siliconindia.com/news/ general/Tumkur-A-Smart-City-in-the-Making-nid-206312-cid-1.html (accessed on 9 November 2019). 83. Estoque, R.C.; Murayama, Y. Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015). ISPRS J. Photogramm. Remote Sens. 2017, 133, 18–29. [CrossRef] 84. Zhang, X.; Estoque, R.C.; Murayama, Y. An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables. Sustain. Cities Soc. 2017, 32, 557–568. [CrossRef] 85. Estoque, R.C.; Murayama, Y.; Myint, S.W. Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia. Sci. Total Environ. 2017, 577, 349–359. [CrossRef] 86. Thapa, R.; Murayama, Y. Urban mapping, accuracy, & image classification: A comparison of multiple approaches in Tsukuba City, Japan. Appl. Geogr. 2009, 29, 135–144. [CrossRef] 87. Ramaiah, M.; Avtar, R. Urban Green Spaces and Their Need in Cities of Rapidly Urbanizing India: A Review. Urban Sci. 2019, 3, 94. [CrossRef] 88. Rahman, M.M.; Rahman, M.M.; Momotaz, M. Environmental Quality Evaluation in Dhaka City Corporation (DCC)-Using Satellite Imagery. Proc. Inst. Civ. Eng. Urban Des. Plan. 2019, 172, 13–25. [CrossRef] 89. Niemelä, J.; Saarela, S.-R.; Söderman, T.; Kopperoinen, L.; Yli-Pelkonen, V.; Väre, S.; Kotze, D.J. Using the ecosystem services approach for better planning and conservation of urban green spaces: A Finland case study. Biodivers. Conserv. 2010, 19, 3225–3243. [CrossRef] 90. Li, X.; Zhou, W.; Ouyang, Z.; Xu, W.; Zheng, H. Spatial pattern of greenspace affects land surface temperature: Evidence from the heavily urbanized Beijing , China. Landsc. Ecol. 2012, 27, 887–898. [CrossRef] Land 2020, 9, 292 21 of 21

91. Tyrväinen, L.; Pauleit, S.; Seeland, K.; de Vries, S. Benefits and uses of urban forests and trees. In Urban Forests and Trees; Springer: Berlin, Germany, 2005; pp. 81–114. 92. Kong, F.; Yin, H.; Nakagoshi, N. Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban green space: A case study in Jinan City, China. Landsc. Urban Plan. 2007, 79, 240–252. [CrossRef] 93. Kong, F.; Yin, H.; Nakagoshi, N.; Zong, Y. Urban green space network development for biodiversity conservation: Identification based on graph theory and gravity modeling. Landsc. Urban Plan. 2010, 95, 16–27. [CrossRef] 94. Cao, X.; Onishi, A.; Chen, J.; Imura, H. Quantifying the cool island intensity of urban parks using ASTER and IKONOS data. Landsc. Urban Plan. 2010, 96, 224–231. [CrossRef]

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).